export const metadata = { sidebar_position: 1, title: "🟢 Introduction" };

# 🟢 Introduction

This chapter covers how to make completions more reliable, as well as how to
implement checks to ensure that outputs are reliable.

To a certain extent, most
of the previous techniques covered have to do with improving completion
accuracy, and thus reliability, in particular self-consistency(@wang2022selfconsistency).
However, there are a number of other techniques that can be used to improve reliability,
beyond basic prompting strategies.

<Term term="LLM">LLM</Term> have been found to be more reliable than we might expect
at interpreting what a prompt is _trying_ to say when responding to misspelled, badly
phrased, or even actively misleading prompts(@webson2023itscomplicated). Despite
this ability, they still exhibit various problems including hallucinations(@ye2022unreliability),
flawed explanations with <Term term="CoT prompting">CoT prompting</Term> methods(@ye2022unreliability),
and multiple biases including majority label bias, recency bias, and common token
bias(@zhao2021calibrate). Additionally, zero-shot CoT can be particularly biased
when dealing with sensitive topics (@shaikh2022second).

Common solutions to some of these problems include calibrators to remove _a priori_ biases,
and verifiers to score completions, as well as promoting diversity in completions.
